Artificial neural network device and operation method thereof

Patent metadata

Field	Value
Publication number	US-2018225563-A1
Application number	US-201815868889-A
Country	US
Kind code	A1
Filing date	Jan 11, 2018
Priority date	Feb 8, 2017
Publication date	Aug 9, 2018
Grant date	—

Abstract

Provided is an artificial neural network device including pre-synaptic neurons configured to generate a plurality of input spike signals, and a post-synaptic neuron configured to receive the plurality of input spike signals and to generate an output spike signal during a plurality of time periods, wherein the post-synaptic neuron respectively applies different weights in the plurality of time periods according to contiguousness with a reference time period in which input spike signals, which lead generation of the output spike signal from among the plurality of input spike signals, are received.

First claim

What is claimed is:

1 . An artificial neural network device comprising:
pre-synaptic neurons configured to generate a plurality of input spike signals; and a post-synaptic neuron configured to receive the plurality of input spike signals and to generate an output spike signal during a plurality of time periods, wherein the post-synaptic neuron respectively applies different weights in the plurality of time periods according to contiguousness with a reference time period in which input spike signals, which lead generation of the output spike signal from among the plurality of input spike signals, are received.

2 . The artificial neural network device of claim 1 , wherein a first weight is applied to input spike signals received from among the plurality of input spike signals in a first time period after the reference time period, and
a second weight is applied to input spike signals received in a first time period after the reference time period in a second time period after the reference time period.

3 . The artificial neural network device of claim 2 , wherein the first weight is larger than the second weight.

4 . The artificial neural network device according to claim 2 , wherein a first weight has a positive value and a second weight has a negative value.

5 . The artificial neural network device of claim 2 , wherein the post-synaptic neuron comprises:
a weight operation unit configured to:
apply the first weight to the input spike signals received from among the plurality of input spike signals in the first time period; and
apply the second weight to the input spike signals received from among the plurality of input spike signals in the second time period.

6 . The artificial neural network device of claim 5 , wherein the weight operation unit comprises:
first and second shift registers configured to respectively store the first and second weights; a multiplier configured to multiply the input spike signals received in the first time period by the first weight, and to multiply the input spike signals received in the second time period by the second weight; an adder configured to add multiplication results of the multiplier; and a register configured to store an addition result of the adder.

7 . The artificial neural network device of claim 6 further comprising:
an accumulator configured to accumulate the input spike signals received during the reference time period;
a spike signal generating unit configured to generate the output spike signal according to whether an accumulation result by the accumulator exceeds a threshold value; and
a calculating unit configured to calculate the first weight and the second weight according to whether the output spike signal is generated.

8 . The artificial neural network device of claim 7 , further comprising:
a weight storage unit configured to store the first weight and the second weight and to provide the stored weights to the weight operation unit.

9 . The artificial neural network device of claim 7 , wherein the spike signal generating unit comprises:
a spike timing determining configured to determine whether to generate the output spike signal on a basis of the accumulation result by the accumulating unit; a spike magnitude determining unit configured to determine a magnitude of the output spike signal; and a pulse generator configured to generate the output spike signal on a basis of determination results of the spike timing determining unit and the spike magnitude determining unit.

10 . The artificial neural network device of claim 9 , wherein the spike signal generating unit further comprises a threshold value storage unit configured to store the threshold value,
wherein the threshold value is a pre-determined or a variable value.

11 . The artificial neural network device of claim 1 , wherein as input spike signals are received from among the plurality of input spike signals in a time period closer to the reference time period, a larger weight is applied, and
as input spike signals are received from among the plurality of input spike signals in a time period farther from the reference time period, a smaller weight is applied

12 . A method of operation an artificial neural network, which comprises a synaptic neuron configured to generate an output spike signal on a basis of a plurality of input spike signals input for each of a plurality of time periods, the operation method comprising:
detecting a reference time period, from among the plurality of time periods, in which input spike signals leading generation of the output spike signal are received; applying, according to contiguousness with the reference time period, different weights to the input spike signals input in a time period after the reference time period from among the plurality of time periods; and generating the output spike signal on a basis of the input spike signals to which the different weights are applied.

13 . The method of claim 12 , wherein the applying of the weights comprises:
applying a first weight to input spike signals received in a first time period after the reference time period from among the plurality of spike signals; and applying a second weight smaller than the first weight to input spike signals received in a second time period after the first time period from among the plurality of spike signals.

14 . The method of claim 12 , wherein the detecting of the reference time period comprises:
determining whether an accumulated input spike signal exceed a threshold value; and selecting, as the reference time period, a time period in which input spike signals leading to exceed the threshold value are input.

15 . The method of claim 12 , wherein the generating of the output spike signal comprises:
determining a generation timing of the output spike signal on a basis of whether the accumulated input spike signal exceeds the threshold value; determining a magnitude of the output spike signal; and generating the output spike signal on a basis of the determined generation timing of the spike signals and the determined magnitude of the output spike signal.

Assignees

• Electronics & Telecommunications Res Inst

Inventors

• Kim Ju-Yeob
• Kim Byung Jo
• Kim Jin Kyu
• Lee Mi Young
• Kim Seong Min
• Lee Joo Hyun

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/049 Primary

  Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs · CPC title

• G06N3/063 Primary

  using electronic means · CPC title